library(ngsReports)
library(edgeR)
library(magrittr)
library(scales)
library(tidyverse)
library(ggrepel)
library(pander)
theme_set(theme_bw())

Load all FastQC summaries

rawFqc <- list.files("../0_rawData/FastQC/", pattern = "zip$", full.names = TRUE) %>%
    getFastqcData()
trimFqc <- list.files("../1_trimmedData/FastQC/", pattern = "zip$", full.names = TRUE) %>%
    getFastqcData()
alnFqc <- list.files("../2_alignedData/FastQC/", pattern = "zip$", full.names = TRUE) %>%
    getFastqcData()

Read Totals

rt <- list(
    readTotals(rawFqc) %>% mutate(Stage = "Raw"),
    readTotals(trimFqc) %>% mutate(Stage = "Trimmed"),
    readTotals(alnFqc) %>% mutate(Stage = "Aligned")
) %>%
    bind_rows() %>%
    mutate(Sample = str_remove_all(Filename, "(_R1.fq.gz|Aligned.sortedByCoord.out.bam)"),
                 Sample = factor(Sample, levels = str_sort(unique(Sample), numeric = TRUE)),
                 Stage = factor(Stage, levels = c("Raw", "Trimmed", "Aligned")))

Summary of library sizes after each step. The slight increase after alignments indicates a low level of multiple alignments

  • Multiple alignments don’t appear to be much of an issue
  • Trimming made little change to the library sizes
  • All samples were run in technical duplicates which can be combined for DE analysis

GC Content

No unusual behaviour was found in GC content.

*GC content for raw data*

GC content for raw data

*GC content for aligned data*

GC content for aligned data

Duplication Levels

No issues were evident with regard to duplication levels

*Duplication levels for raw data*

Duplication levels for raw data

*Duplication levels for aligned data*

Duplication levels for aligned data

Alignment Rate

Alignment rates were also very good

alnLogs <- list.files("../2_alignedData/logs/", full.names = TRUE, pattern = "final.out") %>%
    importStarLogs()
*Alignment summaries for all libraries (before merging replicates)*

Alignment summaries for all libraries (before merging replicates)

Gene-level Counts

MDS Plot

Gene-level counts were loaded without filtering and an MDS plot generated in order to check whether replicate libraries grouped correctly.

counts <- read_tsv("../2_alignedData/featureCounts/genes.out") %>%
    set_names(basename(colnames(.))) %>%
    set_names(str_remove_all(colnames(.), "Aligned.sortedByCoord.out.bam"))
dge <- counts %>% 
    as.data.frame() %>%
    column_to_rownames("Geneid") %>%
    DGEList() %>%
    calcNormFactors()
dge$samples %<>%
    rownames_to_column("library") %>%
    mutate(Genotype = str_extract_all(library, "(FAD|FS|__)"),
                 Genotype = str_replace_all(Genotype, "__", "WT"),
                 Genotype = factor(Genotype, levels = c("WT", "FS", "FAD")),
                 Sample = str_replace(library, "__-", "_WT"),
                 Sample = ifelse(str_count(Sample, "_") == 3, Sample, paste0(Sample, "_1")),
                 Replicate = str_extract(Sample, "[12]$"),
                 Sample = str_remove(Sample, "_[12]$"),
                 group = as.integer(Genotype)) %>%
    column_to_rownames("library")
mds <- plotMDS(dge, plot = FALSE) %>%
    extract2("cmdscale.out") %>%
    set_colnames(paste0("Dim", 1:2)) %>%
    as.data.frame() %>%
    rownames_to_column("Library") %>%
    as_tibble()
*Plot of replicate libraries showing good concordance between replicates. These will be combined and low-expressed genes removed for the DE analysis. However, minimal separation between groups was noted in this pre-filtered stage.*

Plot of replicate libraries showing good concordance between replicates. These will be combined and low-expressed genes removed for the DE analysis. However, minimal separation between groups was noted in this pre-filtered stage.

Library sizes

Library sizes after summarising to the gene-level were also checked.

counts <- read_tsv("../2_alignedData/featureCounts/genes.out") %>%
    set_names(basename(colnames(.))) %>%
    set_names(str_remove_all(colnames(.), "Aligned.sortedByCoord.out.bam"))
*Total counts from each library*

Total counts from each library

Libraries showing the lowest and highest assignment rate to genes as a percentage. All other samples were somewhere between these values for alignments assigned to genes during counting by featureCounts. This was considered to be normal
Library Total Alignments Counts Percent
13__-_3 17,605,122 11,393,459 64.72%
2_FAD_4 23,157,372 16,034,737 69.24%